Identification of Surgery Indicators by Mining Hospital Data: A Preliminary Study

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Marie Persson, Niklas Lavesson
Title: Identification of Surgery Indicators by Mining Hospital Data: A Preliminary Study
Translated title: Identifikation av Operationsindikatorer genom Informationsutvinning av patientdata: en preliminär studie
Conference name: 20th International Workshop on Database and Expert Systems Applications
Year: 2009
Pagination: 323-327
ISBN: 978-0-7695-3763-4
Publisher: IEEE Press
City: Linz, Austria
URI/DOI: 10.1109/DEXA.2009.69
ISI number: 000275655300061
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
http://www.bth.se/com
Authors e-mail: marie.pesson@bth.se, niklas.lavesson@bth.se
Language: English
Abstract: The management of patient referrals is an interesting issue when it comes to predicting future patient demand to increase hospital productivity. In general, a patient is referred from the general practitioner to hospital care. A patient referral contains information that indicates the need for hospital care and this information is differently structured for different medical needs. In practice, these needs can be viewed as the forthcoming patient demand at the hospital, analogous to a volume of orders. Today, the structure of the referrals is very much up to the general practitioner who is referring the patient. This implies that the data provided to the hospital can vary extensively between cases. We suggest that, by enforcing a certain structure on the referral data, it may be possible to make early predictions about the patient demand. Such predictions could then be used as a basis for managing resources more efficiently to increase hospital productivity. This paper investigates the possibility of using data mining techniques to automatically generate prediction models by extracting conclusive information from patient records combined with surgical suite statistics, ,e.g., surgery preparations and anesthesia type, that are of significance for estimating patient demand in a surgery department, e.g., probability of surgery, surgery duration and recovery. We hypothesize that the generated models may provide new knowledge about, and a basis for, how to structure a patient referral. In addition, these models may also be used for the actual prediction of patient demand.
Subject: Computer Science\Artificial Intelligence
Medical Sciences
Keywords: surgery, indicators, data mining, supervised learning, healthcare, hospital
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